"The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations" in "Hello, TensorFlow!" program

[bingq]

Opening this with reference to #7500.

Installed TensorFlow 1.0 with reference to https://www.tensorflow.org/install/install_windows on Windows 10 and hit the same issue discussed in #7500. With applying the solution suggested in that thread, the original issue disappeared but got the new warnings:

C:\Users\geldqb>python
Python 3.5.3 (v3.5.3:1880cb95a742, Jan 16 2017, 16:02:32) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.

import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
2017-02-22 22:28:20.696929: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.
2017-02-22 22:28:20.698285: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations.
2017-02-22 22:28:20.700143: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
2017-02-22 22:28:20.700853: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-02-22 22:28:20.701498: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-02-22 22:28:20.702190: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-02-22 22:28:20.702837: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-02-22 22:28:20.703460: W c:\tf_jenkins\home\workspace\nightly-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
print(sess.run(hello))
b'Hello, TensorFlow!'

[Carmezim]

Those are simply warnings. They are just informing you if you build TensorFlow from source it can be faster on your machine. Those instructions are not enabled by default on the builds available I think to be compatible with more CPUs as possible.
If you have any other doubts regarding this please feel free to ask, otherwise this can be closed.

edit: To deactivate these warnings as @yaroslavvb suggested in another comment, do the following:

import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf

or if you're on a Unix system simply do export TF_CPP_MIN_LOG_LEVEL=2.

TF_CPP_MIN_LOG_LEVEL is a TensorFlow environment variable responsible for the logs, to silence INFO logs set it to 1, to filter out WARNING 2 and to additionally silence ERROR logs (not recommended) set it to 3

[bingq]

Thanks Camezim !
Any hint of how to kill those warnings?

[yaroslavvb]

@bingq the best way I found to kill unwanted TF output:

run your script as tf.sh myscript.py where tf.sh contains

#!/bin/sh
# Run python script, filtering out TensorFlow logging
# https://github.com/tensorflow/tensorflow/issues/566#issuecomment-259170351
python $* 3>&1 1>&2 2>&3 3>&- | grep -v ":\ I\ " | grep -v "WARNING:tensorflow" | grep -v ^pciBusID | grep -v ^major: | grep -v ^name: |grep -v ^Total\ memory:|grep -v ^Free\ memory:

You can add extra |grep -v parts to get rid of more things

[tomrunia]

@Carmezim Any estimate of how much faster when compiling from source using advanced CPU instructions? Any reason to do this at all when running most of the graph on a GPU?

[Carmezim]

@tomrunia I haven't tested myself yet (actually am building it now with SSE) although heard 4-8x. If @yaroslavvb wants to chime in as he himself got 3x speed improvement.

[yaroslavvb]

Yup, 3x for large matrix multiply on Xeon-V3, I expect it's probably due to FMA/AVX rather than SSE

[Carmezim]

@tomrunia Well pointed by @yaroslavvb, not SSE specifically in his case although those CPU instructions are expected to provide performance improvement

[astrojuanlu]

It would be very nice to silence these warnings from the Python side, it's not so easy to use grep from Windows.

[yaroslavvb]

Try export TF_CPP_MIN_LOG_LEVEL=2

[astrojuanlu]

Try export TF_CPP_MIN_LOG_LEVEL=2

Thanks @yaroslavvb, I haven't tried it yet but an environment variable is definitely more useful.

[e-freiman]

It works, thank you

[hughsando]

Is it possible that these errors are coming from the fact that with MSVC, x64, SSE2 is implicit (all x64 chips have SSE2) but the __SSE2__ et al defines are not explicitly set?
Perhaps the guards should be on EIGEN_VECTORIZE_SSE2 etc instead.

[e-freiman]

Could you say what should I exactly do according to your idea? What does "guards on" mean?